The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
2026-07-27 • Emerging Technologies
Emerging TechnologiesArtificial IntelligenceHardware ArchitectureDistributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors describe SpiNNaker2, a new chip designed to combine the strengths of brain-inspired neuromorphic computing and traditional deep learning networks. This chip has many small processors working together, enabling it to handle complex tasks efficiently and with low power use. It can simulate large brain-like networks and also run deep learning models quickly, making it flexible for different types of AI workloads. The chip’s design allows researchers to explore new ways of computing that mimic how the brain works while still supporting popular AI methods.
Neuromorphic computingDeep learningSpiNNaker2Spiking neural networksEvent-based communicationProcessing elementsINT8 workloadsTOPS (tera operations per second)ARM M4F processorLPDDR4 memory
Authors
Stefan Scholze, Johannes Partzsch, Sebastian Höppner, Florian Kelber, Andreas Dixius, Marco Stolba, Sirine Arfa, Marc Berthel, Georg Ellguth, Jim Garside, Hector A. Gonzalez, Stephan Hartmann, Thomas Kiel-Hocker, Dongwei Hu, Matthias Jobst, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Gengting Liu, Matthias Lohrmann, Mantas Mikaitis, Felix Neumärker, Amirhossein Rostami, Stefan Schiefer, Tilo Schubert, Delong Shang, Bernhard Vogginger, Yexin Yan, Steve Furber, Christian Mayr
Abstract
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an upcoming alternative to deep networks, taking inspiration from the brain for achieving unprecedented energy efficiency. However, demonstrations of these gains only recently began to grow in complexity and real-world applicability. With SpiNNaker2, we present a chip that bridges the gap between deep networks and neuromorphic computing and allows for flexible exploration of computing approaches that combine both worlds. It features 152 processing elements equipped with an ARM M4F processor and dedicated accelerators, an extended SpiNNaker routing fabric for scalable event-based communication and a range of external interfaces for system integration, including Gbit Ethernet and an LPDDR4 memory interface. We demonstrate performance and efficiency of the SpiNNaker2 chip for neuromorphic and deep network workloads, as well as novel event-based computing approaches. For deep network workloads, the chip achieves up to 4.5 TOPS in high performance mode and up to 2.7 TOPS/W efficiency in high efficiency mode for INT8 workloads. The chip supports spiking neural networks with >150000 neurons and >1.8 billion synaptic events/s when simulated with a 1 ms time step. Its low baseline power of less than 250 mW allows for efficiency even under varying workload conditions, allowing to explore sparse and event-based modes of computation. All this demonstrates the chip's capabilities as a universal hardware platform for scalable brain-inspired computing and its combinations with mainstream deep network approaches.